SOTAVerified

Graph Learning

Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. In this context, a graph is a collection of nodes (or vertices) and edges, where nodes represent entities and edges represent the relationships or interactions between these entities. This structure is particularly useful for modeling complex networks found in various domains such as social networks, biological networks, and communication networks.

Graph learning leverages the relationships and structures within the graph to learn and make predictions. It includes techniques like graph neural networks (GNNs), which extend the concept of neural networks to handle graph-structured data. These models are adept at capturing the dependencies and influence of connected nodes, leading to more accurate predictions in scenarios where relationships play a key role.

Key applications of graph learning include recommender systems, drug discovery, social network analysis, and fraud detection. By utilizing the inherent structure of graph data, graph learning algorithms can uncover deep insights and patterns that are not apparent with traditional machine learning approaches.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1075 of 1570 papers

TitleStatusHype
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
Graph Contrastive Learning with Cross-view Reconstruction—0
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions—0
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting—0
Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy—0
H^2GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs—0
Trustworthy Graph Neural Networks: Aspects, Methods and Trends—0
TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation—0
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization—0
Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness—0
Uncertainty-Aware Robust Learning on Noisy Graphs—0
Uncertainty in Graph Neural Networks: A Survey—0
Uncertainty Quantification on Graph Learning: A Survey—0
Uncovering Insurance Fraud Conspiracy with Network Learning—0
Understanding Graph Learning with Local Intrinsic Dimensionality—0
Unfolded Deep Graph Learning for Networked Over-the-Air Computation—0
Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network—0
Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems—0
Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search—0
Unifying Graph Contrastive Learning via Graph Message Augmentation—0
Unifying Invariance and Spuriousity for Graph Out-of-Distribution via Probability of Necessity and Sufficiency—0
Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency—0
Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning—0
Deep Graph Clustering via Mutual Information Maximization and Mixture Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified